What role does digital transformation play in physics AI?
Yogi Schulz June 29, 2026
Physics AI is a powerful engineering tool based on a foundation of digital transformation.
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What type of interconnects and connectors link accelerator cards in AI data centers?
Aharon Etengoff May 13, 2026
Many data centers are packed with racks of high-performance graphics processing units (GPUs) and tensor processing units (TPUs). These accelerators process massive artificial intelligence (AI) and machine learning (ML) datasets, executing complex operations in parallel and exchanging data at high speed. This article explores the interconnects and connectors that link AI accelerator clusters together.
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What is the role of liquid cooling connectors in AI data centers?
Aharon Etengoff May 13, 2026
Artificial intelligence (AI) and machine learning (ML) applications consume significant power and generate considerable heat in data centers.
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How is physical artificial intelligence used to optimize data center efficiency?
Jeff Shepard May 12, 2026
PAI is being used for data center optimization to support the demands of digital AI (DAI) applications like training large language models (LLMs), running inference for real-time applications, and supporting infrastructure like power-hungry GPUs and memory.
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How do heterogeneous integration and chiplets support generative AI?
Jeff Shepard June 17, 2026
Chiplets are here, and more are coming. They can overcome the yield limitations of large ASICs, support a mix-and-match strategy for heterogeneous semiconductor IPs and multiple process nodes, improve thermal performance, and speed time to market. They are being used in a range of high-performance computing (HPC) applications, notably generative artificial intelligence (AI) and machine […]
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How twin axial cable assemblies support high-performance computing for AI/ML systems
Jeff Shepard June 17, 2026
The high-performance computing platforms used for artificial intelligence (AI) and machine learning (ML) in hyperscale data centers need high-speed interconnects like 112 Gbps PAM 4 and faster inside the servers. High-speed interconnects are also required between the servers and storage devices. Twin axial (Twinax) cable assemblies are one way to address those needs. This article […]
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Beyond SDVs: how AI optimizes electric vehicles
Aharon Etengoff June 17, 2026
Many automotive manufacturers classify new cars and trucks as software-defined vehicles (SDVs). As SDVs by design, electric vehicles (EVs) optimize vital systems and functions with sophisticated artificial intelligence (AI) and machine learning (ML) capabilities. This article discusses AI’s crucial role in EVs, from smart charging and advanced driver assistance systems (ADAS) to predictive maintenance and…
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Accelerating high-performance AI workloads with photonic chips
Aharon Etengoff May 13, 2026
Artificial intelligence (AI) and machine learning (ML) continue to push the limits of conventional semiconductor architectures. To increase speeds, lower latency, and optimize power consumption for high-performance workloads, semiconductor companies, and research institutions are developing advanced photonic chips that operate on the principles of light rather than electrical currents.
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How to minimize design cycles for AI accelerators with advanced DFT and silicon bring-up
Aharon Etengoff May 13, 2026
Design for testability (DFT) embeds testable features into an integrated circuit (IC) during design, while silicon bring-up initiates chip evaluation and debugging. Streamlining these sequential processes minimizes design cycles and shortens time-to-market (TTM) for advanced artificial intelligence (AI) accelerators.
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What are the hardware strategies for building energy-efficient AI accelerators?
Rakesh Kumar May 13, 2026
Artificial intelligence (AI) applications are spreading to more industries every day. However, the amount of energy used by these AI systems has become a significant issue. Modern deep neural networks require a considerable amount of computing power.
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How to approach AI hardware design to address the memory wall?
Rakesh Kumar May 13, 2026
The transition from general-purpose computing to AI-specific hardware is driven by the specific computational and energy requirements of deep learning models. As these models scale to trillions of parameters, traditional architectures face the memory wall, where the energy required for data movement between memory and processing units significantly exceeds the energy consumed by the computation itself.
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What is ‘compute-in-memory’ and why is it important for AI?
Rakesh Kumar May 13, 2026
This article explains what compute-in-memory (CIM) technology is and how it works. We will examine how current implementations are already delivering significantly better efficiency improvements compared to conventional processors. We will also explore why this new approach could change AI computing.
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What is the HPC memory wall and how can you climb over it?
Jeff Shepard May 13, 2026
The high-performance computing (HPC) memory wall generally refers to the growing disparity between processor speed and memory bandwidth. When processor performance outpaces memory access speeds, this creates a bottleneck in overall system performance, particularly in memory-intensive applications like artificial intelligence (AI).
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What is an AI governor and how does it relate to physical AI?
Jeff Shepard May 12, 2026
An AI governor is a framework, set of policies, or an oversight mechanism designed to ensure that the development and use of AI systems are ethical, safe, transparent, and compliant with legal and societal standards. The term can also refer to an actual piece of code or circuit (governor logic) used as a safety mechanism […]
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How does the IEEE MagNet Challenge use AI for power magnetics modeling?
Jeff Shepard March 27, 2026
The IEEE Power Electronics Society (PELS) Google-Tesla MagNet Challenge is an annual competition. It’s designed to accelerate innovation in magnetic modeling using artificial intelligence (AI). This article reviews some of the highlights from the first two MagNet Challenges in 2023 and 2024. The first installment ran from February to December 2023, with the winners announced […]
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A 4-level framework for AI in PLM, part 2
Diego Tamburini November 18, 2025
Orchestration, custom models, and strategic guidance.
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How to approach AI hardware design to address the memory wall?
Rakesh Kumar May 13, 2026
The transition from general-purpose computing to AI-specific hardware is driven by the specific computational and energy requirements of deep learning models. As these models scale to trillions of parameters, traditional architectures face the memory wall, where the energy required for data movement between memory and processing units significantly exceeds the energy consumed by the computation itself.
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